1. Field of the Invention
The present invention generally relates to a waveform observation system and, more particularly, to a waveform observation system such as an optical time domain reflectometer (to be referred to as an OTDR hereinafter) used in a noise reduction device by a smoothing process.
2. Description of the Related Art
In recent years, in the field of measuring equipment, various waveform observation apparatuses have been realized. A high-precision waveform observation apparatus having accurate observation is strongly demanded.
The above-described OTDR is known as one of these waveform observation apparatuses.
The OTDR incidences an optical pulse into an optical fiber to be measured and converts a reflection optical pulse reflected from a fault point of the optical fiber and a backscattering light generated in the optical fiber into an electric signal at a light-receiving section so as to detect the electric signal. A predetermined operating process is executed to the detected signal to measure the optical loss or fault point of the optical fiber to be measured.
In an OTDR of this type, since the backscattering light is represented as a very smally detection signal, an S/N ratio of the detection signal must be increased by a digital averaging method using an A/D converter, and the signal buried in noise must be detected without waveform distortion.
Since an increase in S/N ratio by a noise reduction technique using a digital averaging process depends on the number of averaging processes and the number of bits of the A/D converter, a time for a measuring process is practically restricted, and an actual increase in S/N ratio is limited due to actual restriction of a measuring processing time and quantization noise. For this reason, in the OTDR using this technique, especially, it may be difficult to accurately measure a long-distance optical fiber.
Of operating techniques for reducing a noise component included in a waveform to be measured without influence of restriction of a measuring time by a smoothing process to extract a target signal component, a technique called a shift averaging method is known.
This shift averaging method performs the following processes to digital waveform data to reduce noise.
When the waveform data has n discrete values x(i) (i=1, 2, 3, . . . , n), a resultant value of x(i), i.e., an average value y(i) is calculated using a "weighting function" w(j) (j=-m, . . . , -1, 0, 1, . . . , m) constituted by N (=2m+1) discrete points as follows: ##EQU1## When the weighting function w(j) to be used has a rectangular shape shown in FIG. 2A, a method using this weight function is called a simple shift averaging method. When the weighting function w(j) to be used is a function shown in FIG. 2B, a method using this function is called a polynomial adaptation method.
As is apparent from equation 1, in the shift averaging method, a weighted averaging process is performed to the N data having the value x(i) as a center by the function w(j), thereby calculating the averaged value y(i).
In the shift averaging method for performing the above process, when a waveform level is rapidly changed in a smoothing period N having data x(i) at a middle portion thereof obtained by the width of the weighting function, the change disadvantageously becomes moderate at the positive or negative edge of the rectangular pulse waveform, i.e., the waveform becomes rounded.
In order to solve the above drawback of the shift averaging method, an adaptive smoothing method is used.
That is, in the adaptative smoothing method, a variance .sigma..sub.n.sup.2 (i) of noise of waveform data and a variance .sigma..sub.x.sup.2 (i) of waveform data in a smoothing period N are calculated, thereby calculating a smoothing value y(i) as follows: ##EQU2## where x(i) is an average value obtained by performing the simple shift averaging method to the N waveform data.
The above adaptative smoothing method has characteristics as follows. If .sigma..sub.x.sup.2 (i).apprxeq..sigma..sub.n.sup.2, then y(i).apprxeq.x(i), and if .sigma..sub.x.sup.2 (i)&gt;&gt;.sigma..sub.n.sup.2, then y(i).apprxeq.x(i).
That is, according to the adaptative smoothing method, due to the above characteristics, when a waveform level is rapidly changed in the smoothing period N (i.e., .sigma..sub.x.sup.2 (i)&gt;&gt;.sigma..sub.n.sup.2), y(i)=x(i) is obtained. An original waveform is directly used as a smoothing value, and the waveform is not rounded.
In this case, however, a noise component is directly output as the smoothing value, noise is not advantageously reduced.
When a waveform is rounded or noise is not effectively reduced in an OTDR, a marker cannot be desirably adjusted to a predetermined waveform point not to accurately detect a fault point of an optical fiber.
As described above, in the noise reduction technique by a smoothing process free from an influence of restriction of measuring time, although a simple shift averaging method and a polynomial application method included in a shift averaging method is effective to noise reduction, a signal waveform is rounded. In addition, in the adaptative smoothing method, although the signal waveform is not rounded, when the level of the waveform is rapidly changed, noise is not reduced at the rapidly changed portion of the waveform.
Therefore, the noise reduction technique by a smoothing process cannot be directly applied to an OTDR.